Agent System Mining: Vision, Benefits, and Challenges

Andrei Tour,Artem Polyvyanyy,Anna Kalenkova

doi:10.1109/access.2021.3095464

Andrei Tour, Artem Polyvyanyy + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3095464

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Abstract

In the Agent-Based Modeling (ABM) paradigm, an organization is a Multi-Agent System (MAS) composed of autonomous agents inducing business processes. Process Mining automates the creation, update, and analysis of explicit business process models based on event data. Process Mining techniques make simplifying assumptions about the processes discovered from data. However, actual business processes are often more complex than those restricted by Process Mining assumptions. Several Process Mining approaches relax these standard assumptions by discovering more realistic process models. These approaches can discover more realistic process models. However, these models are often difficult to visualize and, consequently, to understand. Many MASs induce processes whose behaviors become more complex with each next embraced time step, while the complexities of these MASs remain constant. Thus, the ABM paradigm can cope naturally with the increasing complexity of the discovered process models. This paper proposes Agent System Mining (ASM) and ASM Framework. ASM combines Process Mining and ABM in the Business Process Management (BPM) context to infer MAS models of operational business processes from real-world event data, while ASM Framework maps ASM activities to different phases of the MAS modeling lifecycle. The paper also discusses the benefits of using ASM and outlines challenges associated with the implementation of the ASM Framework.

Highlights

B USINESS PROCESS MANAGEMENT (BPM) is concerned with improving the operational performance of organizations through the BPM lifecycle [1]
Process Mining automates activities involved in creating, updating, and analyzing the explicit process models based on the knowledge about the real-world operational processes extracted from current and historical event data managed by information systems [2]
Process Mining techniques use event logs as their input. These event logs are recordings of operational processes captured by the information systems

Summary

INTRODUCTION

B USINESS PROCESS MANAGEMENT (BPM) is concerned with improving the operational performance of organizations through the BPM lifecycle [1]. To produce models that encode the self-organizing behaviors, Process Mining techniques must relax the single control flow assumption that posits the total order and casual dependency for all events belonging to the same process instance. ASM supports constructing compact agent-based representations of emergent real-world business processes This ability is based on the property of agent-based models to simulate the non-decreasing complexity of the behavior of self-organizing socio-technical systems [15]. It provides a different perspective for analyzing processes that helps to study the macro-level business process impact of micro-level changes.

MOTIVATING EXAMPLE

AGENTS AND MULTI-AGENT SYSTEMS

AGENT SYSTEM MINING

RELATED WORK

CONCLUSION